Large data sets may exist in various sizes and with various levels of organization. With big data comprising data sets as large as ever, the volume of data collected incident to the increased popularity of online and electronic transactions continues to grow. Billions of rows and hundreds of thousands of columns worth of data may populate a single table, for example. An example of the use of big data is in identifying and categorizing business spending and consumer spending, which is frequently a key priority for transaction card issuers. In that regard, transactions processed by the transaction card issuer are massive in volume and comprise tremendously large data sets.
Large data sets may have challenges. For example, cardholders may frequently hold a business-oriented transaction card, but various merchants may or may not accept the business-oriented transaction card. Similarly, cardholders may hold a consumer-oriented transaction card, but may complete business transactions using the card. These actions confuse and frustrate the identification and categorization of transaction data, and obscure the identity and categorization of real-world entities and individuals behind transactions, while also hampering data analytics.